A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation

  • Safia DjemameEmail author
  • Mohamed Batouche


This chapter presents a new algorithm for edge detection based on the hybridization of quantum computing and metaheuristics . The main idea is the use of cellular automata (CA) as a complex system for image modeling, and quantum algorithms as a search strategy. CA is a grid of cells which cooperate in parallel and have local interaction with their neighbors using simple transition rules. The aim is to produce a global function and exhibit new structures. CA is used to find a subset of a large set of transition rules, which leads to the final result, in our case: edge detection. To tackle this difficult problem, the authors propose the use of a Quantum Genetic Algorithm (QGA) for training CA to carry out edge detection tasks. The efficiency and the enforceability of QGA are demonstrated by visual and quantitative results. A comparison is made with the Conventional Genetic Algorithm . The obtained results are encouraging.


Metaheuristics Quantum computing Quantum genetic algorithm Complex systems Image segmentation Edge detection Cellular automata Rule optimization 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science Department, Faculty of SciencesUniversity of Ferhat ABBAS-Setif1SetifAlgeria
  2. 2.College of NTICUniversity of Constantine 2ConstantineAlgeria

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